Online Angular Super-resolution with Total Variation Regularization for Airborne Scanning Radar

被引：0
作者：
Zhao, Xian [1 ,2 ]
Mao, Deqing [1 ]
Wang, Wenjing [1 ]
Zhang, Yongchao [1 ,2 ]
Zhang, Yin [1 ,2 ]
Huang, Yulin [1 ,2 ]
Yu, Jianyu [1 ]
机构：
[1] Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China
[2] UESTC, Yangtze Delta Reg Inst, Quzhou, Zhejiang, Peoples R China
来源：
2024 IEEE RADAR CONFERENCE, RADARCONF 2024 | 2024年
关键词：
Super-resolution; Total variation (TV) regulation; Online update framework;
D O I：
10.1109/RADARCONF2458775.2024.10548894
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
Total variation (TV) regularization method has been used in forward imaging of airborne scanning radar. However, the traditional regularization method usually processes the whole echo matrix line by line, and every processing includes inversion operation, which leads to huge computational complexity and insufficient real-time. To solve this problem, an improved TV regularization method is proposed for online real aperture radar super-resolution. By using the correlation between adjacent echoes, the matrix inversion operation is transformed into the iterative operation of matrix multiplication, and the real-time scanning echoes are used to effectively update the reconstruction results online, and the real-time online update of the target in the forward view area is realized. Compared with the traditional batch processing method, the proposed method significantly reduces the computational complexity without losing the imaging performance, saves the computing time, and is suitable for the fast imaging of scanning radar.
引用
收藏
页数：5
相关论文
共 10 条
[1]   Hyperspectral Image Denoising Using Spatio-Spectral Total Variation [J].
Aggarwal, Hemant Kumar ;
Majumdar, Angshul .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (03) :442-446
[2]   Non-Parametric High-Resolution SAR Imaging [J].
Glentis, George Othon ;
Zhao, Kexin ;
Jakobsson, Andreas ;
Li, Jian .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2013, 61 (07) :1614-1624
[3]   Total Variation Regularization With Split Bregman-Based Method in Magnetic Induction Tomography Using Experimental Data [J].
Li, Fang ;
Abascal, Juan F. P. J. ;
Desco, Manuel ;
Soleimani, Manuchehr .
IEEE SENSORS JOURNAL, 2017, 17 (04) :976-985
[4]   Angular Superresolution of Real Aperture Radar Using Online Detect-Before-Reconstruct Framework [J].
Mao, Deqing ;
Yang, Jianyu ;
Zhang, Yongchao ;
Huo, Weibo ;
Luo, Jiawei ;
Pei, Jifang ;
Zhang, Yin ;
Huang, Yulin .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[5]   NONLINEAR TOTAL VARIATION BASED NOISE REMOVAL ALGORITHMS [J].
RUDIN, LI ;
OSHER, S ;
FATEMI, E .
PHYSICA D, 1992, 60 (1-4) :259-268
[6]   New Method of Sparse Parameter Estimation in Separable Models and Its Use for Spectral Analysis of Irregularly Sampled Data [J].
Stoica, Petre ;
Babu, Prabhu ;
Li, Jian .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2011, 59 (01) :35-47
[7]   Sparse Target Batch-Processing Framework for Scanning Radar Superresolution Imaging [J].
Tuo, Xingyu ;
Mao, Deqing ;
Zhang, Yin ;
Zhang, Yongchao ;
Huang, Yulin ;
Yang, Jianyu .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2023, 20
[8]   Superresolution of Coherent Sources in Real-Beam Data [J].
Uttam, Shikhar ;
Goodman, Nathan A. .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2010, 46 (03) :1557-1566
[9]   Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares [J].
Yardibi, Tarik ;
Li, Jian ;
Stoica, Petre ;
Xue, Ming ;
Baggeroer, Arthur B. .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2010, 46 (01) :425-443
[10]   FAST MAJORIZE-MINIMIZATION BASED SUPER-RESOLUTION ALGORITHM FOR RADAR FORWARD-LOOKING IMAGING [J].
Yin, Xichen ;
Liu, Lin ;
Huang, Yulin ;
Feng, Mengxi ;
Zhang, Yin ;
Yang, Jianyu .
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, :2055-2058
1